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Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 8551–8580 of 9403 results
- TruefoundryMediumSoftware EngineerQuery a Versioned Array
This problem tests your ability to design a data structure that supports efficient state snapshots and point queries, specifically focusing on space-time trade-offs.
Coding & AlgorithmsOnsiteData StructuresBinary SearchArrays - TruefoundryMediumSoftware EngineerVerify Ordering Under a Scrambled Alphabet
This problem tests string manipulation, hash map usage for indexing, and the ability to compare two strings based on custom character priority.
Coding & AlgorithmsOnsiteStringsSortingHash Map - TubiMediumData ScientistCompute daily net users from event logs
This question evaluates SQL-based data manipulation skills focused on daily aggregation, cohort and retention calculations, deduplicating event records, and grouping metrics by device_platform.
Data Manipulation (SQL/Python)Technical Screen - TubiHardData ScientistDetermine A/B test sample size drivers
This question evaluates statistical power and sample size determination for A/B testing, covering hypothesis testing for proportions, derivation using normal approximations, the impact of MDE, α and power choices, allocation ratios, cluster design (ICC and design effect), group‑sequential monitoring, and adjustment for overdispersed count outcomes.
Analytics & ExperimentationTechnical Screen - TubiMediumSoftware EngineerExecute TTL Cache Operations with Fetch-on-Miss
Implement a deterministic TTL cache simulator with put, get, delete, size, and fetch-on-miss operations. Preserve expired records until deletion or replacement, model loader success and failure explicitly, and validate timestamped JSON-compatible inputs.
Coding & AlgorithmsTechnical Screen - TubiMediumMachine Learning EngineerExplain ML basics and recommender tuning
This question evaluates core machine learning competencies including overfitting and regularization, ensemble methods like bagging, linear and logistic regression, transformer architectures, optimizer trade-offs (SGD vs Adam), hyperparameter tuning in production, and end-to-end recommender system design and deployment.
Machine LearningTechnical Screen - TubiHardData ScientistHandle multiple experiment metrics and communication
This question assesses the ability to design a multi-metric experiment, manage the false positive rate under multiple testing, and communicate trade-off decisions to non-technical stakeholders.
Analytics & ExperimentationTechnical ScreenA/B TestingStatistical SignificanceMultiple Comparisons - TubiMediumMachine Learning EngineerImplement K-Means and compare with GMM
This question evaluates understanding and practical implementation of clustering algorithms, specifically K-Means centroid initialization, assignment and update mechanics, convergence criteria and edge-case handling, along with comparative knowledge of Gaussian Mixture Models and the Expectation-Maximization training framework.
Coding & AlgorithmsTechnical Screen - TubiMediumMachine Learning EngineerML System Design: Movie Recommendation Model and Pipeline (AI-Assisted Round)
This question tests ML system design competency, specifically the ability to architect end-to-end recommendation pipelines covering retrieval, ranking, feature engineering, and low-latency serving at scale. It assesses practical knowledge of two-stage recommender systems, implicit feedback modeling, cold-start handling, and critical evaluation of AI-generated code — skills central to machine learning engineering interviews.
ML System DesignTechnical Screen - TubiMediumMachine Learning EngineerML System Design: Thumbnail Selection for a Streaming Catalog
This ML system design question tests a candidate's ability to architect an end-to-end personalization and ranking system, covering problem framing, reward modeling, and low-latency serving at scale. It evaluates practical knowledge of contextual bandits, engagement-signal design, cold-start handling, and off-policy evaluation — core competencies for machine learning engineering roles.
ML System DesignTechnical Screen - TubiMediumMachine Learning EngineerMachine Learning Fundamentals: Tree Models, Training, Evaluation, and Embeddings
This question assesses foundational machine learning knowledge across multiple core domains, including tree-based models, supervised training, model evaluation, embeddings, and transformer architecture. It is commonly used in ML engineer interviews to gauge whether a candidate can explain key concepts with correct mechanics, articulate trade-offs, and reason through failure modes.
Machine LearningTechnical Screen - TubiMediumSoftware EngineerMake Cache Fetch Thread-Safe and Single-Flight
Design a thread-safe cache fetch operation that coalesces concurrent misses for the same key while allowing different keys to load in parallel. Define linearization points, generation-aware publication, lock scope, timeout and error handling, deletion races, and stale completion cleanup.
Software Engineering FundamentalsTechnical Screen - TubiMediumMachine Learning EngineerWeighted Random Sampling by Index
This question tests a candidate's ability to implement weighted random sampling using prefix sums and binary search, evaluating practical algorithm design for probability distributions. It assesses proficiency in translating mathematical probability constraints into efficient data structures, a skill commonly probed in machine learning engineering roles where non-uniform sampling is fundamental to model training and inference pipelines.
Coding & AlgorithmsTechnical Screen - TudorMediumData ScientistEstimate Volatility from Gaussian and Brownian Samples
This question evaluates skills in statistical estimation and stochastic process modeling, specifically parameter estimation for Gaussian samples and volatility estimation from discrete Brownian motion observations, focusing on estimating the volatility parameter (sigma) or its variance (sigma^2).
Machine LearningTechnical Screen - TuringMediumSoftware EngineerCount permutations with exactly n inversions
This Coding & Algorithms question evaluates combinatorial counting and algorithmic skills related to permutations and inversion counts, emphasizing dynamic programming and modular arithmetic for large-result handling.
Coding & AlgorithmsTechnical Screen - TuroMediumSoftware EngineerCheck if a word exists in a grid
This question evaluates a candidate's skills in backtracking search, grid traversal, and managing state for constrained path-finding problems. Commonly asked to assess algorithmic problem-solving, recursion and memory-management (including in-place visited marking) within the Coding & Algorithms domain, it emphasizes practical application and understanding of time and space complexity trade-offs rather than purely theoretical concepts.
Coding & AlgorithmsTechnical Screen - TuroHardData ScientistCompare XGBoost and LightGBM
This question evaluates a data scientist's understanding of gradient-boosted tree implementations and practical model trade-offs, covering competencies in algorithmic differences, performance and memory characteristics, handling of missing and categorical features, regularization risks, and hyperparameter considerations for production models.
Machine LearningOnsite - TuroMediumSoftware EngineerDesign trip booking REST API and schema
This question evaluates RESTful API design, temporal data modeling, inventory management strategies, request/response/status-code design, data schema and indexing, and handling booking lifecycle changes such as extensions.
System DesignTechnical Screen - TuroHardData ScientistIdentify Booking Drivers
This question evaluates skills in product analytics, causal inference, feature importance modeling, metric definition, and experimental validation within a marketplace context.
Analytics & ExperimentationOnsite - TuroMediumSoftware EngineerPlan a major database version upgrade
This question evaluates competency in database upgrade planning, including data migration strategies, cutover coordination, compatibility considerations, change-data-capture versus dual-write trade-offs, and rollback and risk management.
System DesignTechnical Screen - TuroMediumSoftware EngineerRoute HTTP requests to pods in a cluster
This question evaluates understanding of request routing, service discovery, virtual service abstractions, load balancing algorithms, health checks, and fault-tolerance mechanisms within containerized cluster environments, testing competencies in distributed systems and networked service architecture.
System DesignTechnical Screen - TwitchMediumData ScientistAnalyze Streaming Data with SQL for Monthly Trends
This question evaluates proficiency in SQL time-series aggregation, date extraction and grouping across calendar months, join and conditional aggregation techniques, windowed month-over-month comparisons, and handling sparse or multi-year datasets; Category: Data Manipulation (SQL/Python); level: practical application.
Data Manipulation (SQL/Python)Technical Screen - TwitchHardData ScientistDesign real-time live-stream recommendations
This question evaluates competency in designing real-time recommender systems within the Machine Learning domain, covering objectives and label construction for streaming events, feature engineering across user/creator/stream/context, model and loss choices, bias mitigation, evaluation metrics, and low-latency serving and freshness constraints.
Machine LearningTechnical Screen - TwitchMediumData ScientistExplain motivation and behavioral principles
This question evaluates a data scientist's motivational fit and behavioral leadership competencies, including communication under time pressure, stakeholder management, handling ambiguous requirements, prioritization, experimental design reasoning, and influencing without formal authority.
Behavioral & LeadershipTechnical Screen - TwitchMediumData ScientistSolve advanced SQL for streaming analytics
This question evaluates advanced SQL competency for streaming analytics — covering time-series aggregation, monthly bucketing across year boundaries, joins between event tables, window functions, and case-insensitive substring filtering; the domain is Data Manipulation (SQL/Python) and the level is practical application of SQL techniques.
Data Manipulation (SQL/Python)Technical Screen - TwitchMediumSoftware EngineerSolve four design-leaning coding problems
This multi-part question evaluates algorithmic problem-solving, data structure design and manipulation (such as LRU cache semantics), stateful simulation and scoring logic, chronological quota accounting for billing, and set/map operations for detecting overlaps.
Coding & AlgorithmsOnsite - Two SigmaHardData ScientistAdd two integer strings using digit-adder
This problem tests string manipulation, modular arithmetic, and the ability to manage carry-over logic manually to perform addition on integers that exceed the capacity of standard numeric data types.
Coding & AlgorithmsTechnical ScreenString ManipulationTwo PointersArithmetic - Two SigmaMediumData ScientistAnalyze NYC taxi trips efficiently over last 7 days
This question evaluates proficiency in time-series filtering, relational joins, grouped aggregations (median and high-percentile calculations), numeric stability of percentile methods, and performance-aware vectorized data manipulation using SQL or pandas.
Data Manipulation (SQL/Python)Online Assessment - Two SigmaMediumData ScientistAnalyze Temperatures and Update Regression
This question evaluates proficiency in time-series and statistical analysis (defining volatility and similarity metrics), supervised regression modeling with mean squared error evaluation, greedy feature selection, and streaming/no-intercept linear regression updates.
Machine LearningOnline Assessment - Two SigmaEasyData ScientistAnswer four core statistics questions
This multipart problem evaluates competency in probability and mathematical statistics, covering order statistics and covariance, the rationale for degrees of freedom in sample variance, z-test sample size calculation, and a one-sided concentration inequality, categorized under Statistics & Math for a Data Scientist role and combining conceptual derivations with practical formula application. Such questions are commonly asked to verify mathematical rigor in deriving expectations and covariances, understanding sampling distributions and unbiased estimation, applying hypothesis testing power formulas, and proving probabilistic inequalities that underpin statistical reasoning.
Statistics & MathTechnical Screen
Tubi
Turo
Twitch
Two Sigma